AI-powered literature mining reveals the therapeutic significance of GLP-1 receptor: Simulation of natural agonist candidates based on molecular dynamics
Computational Biology and Chemistry, cilt.121, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 121
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.compbiolchem.2025.108828
- Dergi Adı: Computational Biology and Chemistry
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, BIOSIS, Chemical Abstracts Core, Chimica, Compendex, EMBASE, MEDLINE, zbMATH, Academic Search Ultimate (EBSCO)
- Anahtar Kelimeler: BioBERT, GLP-1 receptor, Machine learning, Molecular dynamics simulation, Natural compounds
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Glucagon-like peptide-1 (GLP-1), a pivotal incretin hormone modulating glycemic homeostasis, has emerged as a clinically validated target for the treatment of type 2 diabetes and obesity. In this study, we present a comprehensive AI-integrated drug discovery pipeline that leverages BioBERT-based biomedical text mining to delineate the therapeutic landscape of GLP-1 receptor agonism systematically. Subsequent high-throughput virtual screening (HTVS) of a curated natural product library identified structurally diverse candidate ligands. A machine-learning-guided ADMET profiling algorithm was employed to prioritize compounds with optimal pharmacokinetic and safety characteristics. Top-ranked molecules were subjected to extensive molecular dynamics (MD) simulations using the GROMACS platform, enabling quantitative evaluation of structural stability, dynamic behavior, and receptor-ligand interaction persistence. Molecular docking analyses demonstrated robust binding affinities (ΔG: −11.3 to −8.7 kcal/mol), while MM-PBSA free energy estimations (ΔG<−30 kcal/mol) corroborated the thermodynamic favorability of binding. Among the screened entities, five lead candidates—CNP0244222.1, CNP0186692.11, CNP0361941.2, CNP0547477.1, and CNP0258197.2—consistently exhibited superior ADMET scores (>0.67), stable interaction trajectories, and enthalpically favorable profiles. This integrative, AI-augmented computational framework demonstrates substantial potential to accelerate the rational design and preclinical advancement of GLP-1-targeted therapeutics.